Practical C# and WPF for Financial Markets provides a complete explanation of .NET programming in quantitative finance. It demonstrates how to implement quant models and back-test trading strategies. It pays special attention to creating business applications and reusable C# libraries that can be directly used to solve real-world problems in quantitative finance. The book contains: • Overview of C#, WPF programming, data binding, and MVVM pattern, which is necessary to create MVVM compatible .NET financial applications. • Step-by-step approaches to create a variety of MVVM compatible 2D/3D charts, stock charts, and technical indicators using my own chart package and Microsoft chart control. • Introduction to free market data retrieval from online data sources using .NET interfaces. These data include EOD, real-time intraday, interest rate, foreign exchange rate, and option chain data. • Detailed procedures to price equity options and fixed-income instruments, including European/American/Barrier options, bonds, and CDS, as well as discussions on related topics such as cash flows, term structures, yield curves, discount factors, and zero-coupon bonds. • Introduction to linear analysis, time series analysis, and machine learning in finance, which covers linear regression, PCA, SVM, and neural networks. • In-depth descriptions of trading strategy development and back-testing, including strategies for single stock trading, stock pairs trading, and trading for multi-asset portfolios.

The models of portfolio selection and asset price dynamics in this volume seek to explain the market dynamics of asset prices. Presenting a range of analytical, empirical, and numerical techniques as well as several different modeling approaches, the authors depict the state of debate on the market selection hypothesis. By explicitly assuming the heterogeneity of investors, they present models that are descriptive and normative as well, making the volume useful for both finance theorists and financial practitioners. Explains the market dynamics of asset prices, offering insights about asset management approaches Assumes a heterogeneity of investors that yields descriptive and normative models of portfolio selections and asset pricing dynamics

Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.

Comprehensive coverage of a highly technical area Financial Trading Systems Design and Development with C++ (+CD-ROM) is divided into four sections: Basic Concepts and Data Model, Financial Modeling, Basic Trading System, and Risk Analysis and Reporting. It offers readers the easy-to-use combination of good software design, practical mathematical models, and widely used business practices for immediate solutions. This valuable resource also includes numerous examples and case studies including a front-to-back description of a credit derivatives system, which explains detailed algorithms and C++ code. Gaurav Mangla (New York, NY) is currently Associate Director at Barclays Capital, where he manages the fixed income sales and research technology team that develops enterprise-wide and Web-based applications for both internal users and external clients. He has several years of experience designing and implementing technology solutions for large investment banks.

PRAISE FOR THE LONG GOOD BUY: "Oppenheimer offers brilliant insights, sage advice and entertaining anecdotes. Anyone wishing to understand how financial markets behave – and misbehave – should read this book now." Stephen D. King, economist and author of Grave New World: The End of Globalisation, the Return of History "Peter has always been one of the masters of dissecting financial markets performance into an understandable narrative, and in this book, he pulls together much of his great thinking and style from his career, and it should be useful for anyone trying to understand what drives markets, especially equities." Lord Jim O'Neill, Chair, Chatham House "A deeply insightful analysis of market cycles and their drivers that really does add to our practical understanding of what moves markets and long-term investment returns." Keith Skeoch, CEO, Standard Life Aberdeen "This book eloquently blends the author's vast experience with behavioural finance insights to document and understand financial booms and busts. The book should be basic reading for any student of finance." Elias Papaioannou, Professor of Economics, London Business School "This is an excellent book, capturing the insights of a leading market practitioner within the structured analytical framework he has developed over many years. It offers a lively and unique perspective on how markets work and where they are headed." Huw Pill, Senior Lecturer, Harvard Business School "The Long Good Buy is an excellent introduction to understanding the cycles, trends and crises in financial markets over the past 100 years. Its purpose is to help investors assess risk and the probabilities of different outcomes. It is lucidly written in a simple logical way, requires no mathematical expertise and draws on an amazing collection of historical data and research. For me it is the best and most comprehensive introduction to the subject that exists." Lord Brian Griffiths, Chairman - Centre for Enterprise, Markets and Ethics, Oxford

This book provides comprehensive details of developing ultra-modern, responsive single-page applications (SPA) for quantitative finance using ASP.NET Core and Angular. It pays special attention to create distributed web SPA applications and reusable libraries that can be directly used to solve real-world problems in quantitative finance. The book contains: Overview of ASP.NET Core and Angular, which is necessary to create SPA for quantitative finance. Step-by-step approaches to create a variety of Angular compatible real-time stock charts and technical indicators using ECharts and TA-Lib. Introduction to access market data from online data sources using .NET Web API and Angular service, including EOD, intraday, real-time stock quotes, interest rates. Detailed procedures to price equity options and fixed-income instruments using QuantLib, including European/American/Barrier/Bermudan options, bonds, CDS, as well as related topics such as cash flows, term structures, yield curves, discount factors, and zero-coupon bonds. Detailed explanation to linear analysis and machine learning in finance, which covers linear regression, PCA, KNN, SVM, and neural networks. In-depth descriptions of trading strategy development and back-testing for crossover and z-score based trading signals.